Peng Xu (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences; University of Chinese Academy of Sciences), Yanhao Wang (QI-ANXIN Technology Research Institute), Hong Hu (Pennsylvania State University), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences)

Scripting languages like JavaScript are being integrated into commercial software to support easy file modification. For example, Adobe Acrobat accepts JavaScript to dynamically manipulate PDF files. To bridge the gap between the high-level scripts and the low-level languages (like C/C++) used to implement the software, a binding layer is necessary to transfer data and transform representations. However, due to the complexity of two sides, the binding code is prone to inconsistent semantics and security holes, which lead to severe vulnerabilities. Existing efforts for testing binding code merely focus on the script side, and thus miss bugs that require special program native inputs.

In this paper, we propose cooperative mutation, which modifies both the script code and the program native input to trigger bugs in binding code. Our insight is that many bugs are due to the interplay between the program initial state and the dynamic operations, which can only be triggered through two-dimensional mutations. We develop three novel techniques to enable practical cooperative mutation on popular scripting languages: we first cluster objects into semantics similar classes to reduce the mutation space of native inputs; then, we statistically infer the relationship between script code and object classes based on a large number of executions; at last, we use the inferred relationship to select proper objects and related script code for targeted mutation. We applied our tool, COOPER, on three popular systems that integrate scripting languages, including Adobe Acrobat, Foxit Reader and Microsoft Word. COOPER successfully found 134 previously unknown bugs. We have reported all of them to the developers. At the time of paper publishing, 59 bugs have been fixed and 33 of them are assigned CVE numbers. We are awarded totally 22K dollars bounty for 17 out of all reported bugs.

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Yi Zhu (State University of New York at Buffalo), Chenglin Miao (University of Georgia), Foad Hajiaghajani (State University of New York at Buffalo), Mengdi Huai (University of Virginia), Lu Su (Purdue University) and Chunming Qiao (State University of New York at Buffalo)

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Ziqi Xu (University of Arizona), Jingcheng Li (University of Arizona), Yanjun Pan (University of Arizona), Loukas Lazos (University of Arizona, Tucson), Ming Li (University of Arizona, Tucson), Nirnimesh Ghose (University of Nebraska–Lincoln)

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Ryan Tsang (University of California, Davis), Doreen Joseph (University of California, Davis), Qiushi Wu (University of California, Davis), Soheil Salehi (University of California, Davis), Nadir Carreon (University of Arizona), Prasant Mohapatra (University of California, Davis), Houman Homayoun (University of California, Davis)

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Dr. Eric Eide (University of Utah)

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